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We report here work done towards development of a two arm, high energy, high power Nd:phosphate glass laser system. One arm of the laser is proposed to be operated in a long pulse (∼1.5ns pulse duration) with an energy of ∼400 J. Presently, this arm of the laser is operating at energy of ∼100 J after a disc amplifier that amplifies the laser beam of diameter ∼94 mm. After the addition of two more disc amplifiers which are nearing completion, the laser beam will have energy of ∼400 J, with a beam diameter 140 mm, at an intensity of ∼2 GW/cm2. This beam will be converted to its second harmonic using a 2 × 2 array KDP crystals. The second arm, under development, will operate with a hybrid amplification scheme using optical parametric chirped pulse amplification (OPCPA) and conventional amplification using the existing Nd:glass amplifiers to produce 50 TW, 25 J, 500 fs pulse. A tiled triangular pulse compressor is under development for compressing the stretched pulse.

In recent years, research into and concern over algorithmic fairness and bias in machine learning systems has grown significantly. It is vital to make sure that these systems are fair, impartial, and do not support discrimination or social injustices since machine learning algorithms are becoming more and more prevalent in decision-making processes across a variety of disciplines. This abstract gives a general explanation of the idea of algorithmic fairness, the difficulties posed by bias in machine learning systems, and different solutions to these problems. Algorithmic bias and fairness in machine learning systems are crucial issues in this regard that demand the attention of academics, practitioners, and policymakers. Building fair and unbiased machine learning systems that uphold equality and prevent discrimination requires addressing biases in training data, creating fairness-aware algorithms, encouraging transparency and interpretability, and encouraging diversity and inclusivity.

Highly intermittent phytoplankton is ubiquitously observed when measurements are performed at micro-scale (< 1mm). The conventional way of plankton modelling is based on the mean-field approach in which only the first central-moment approximations is retained and ignored higher central moments). The conventional modeling approach may be suitable for mesoor bigger scale (km) but it is inappropriate for micro-scale (< 1mm) where observed overlap in the intermittent spatial distributions of predators and prey become more important for determining the flow of nutrients and energy up the food chain. A new modelling approach called closure modelling is developed to account intermittent phytoplankton using Reynold’s decomposition from turbulence theory and retaining higher central moment approximations in Taylor series. In this study, we developed a NPZD compartmental model to describe the interactions of nutrient (N), phytoplankton (P), zooplankton (Z) and detritus (D) using closure modelling which accounts mean and fluctuating parts of these plankton variables. The results obtained in NPZD compartmental model confirm that perturbation / heterogeneity supports higher trophic levels involved in the model. This reassured the earlier results observed in case of NP and NPZ models in which perturbations enhances P-biomass and Z-biomass respectively. It is observed that perturbation / heterogeneity and a statistical quantity called coefficient of variations of phytoplankton (CVp) (ratio of standard deviation and mean) are positively associated in plankton ecosystems. The perturbations / heterogeneity leads to higher transfer efficiency (Z-biomass/P-biomass) in plankton ecosystems. These results are robust i.e. independent of parameters choices. Perturbation / heterogeneity effects on community structure, species richness and may quantify the energy transfer along trophic levels through biological process from primary production to higher trophic levels. Based on our study, we hypothesize that the locations with high (CVp) are highly heterogeneous and have high transfer efficiency, while low (CVp) locations are less heterogeneous around Tokyo Bay.